Digital Health Informatics Framework for Traditional Medical Systems: Transforming Ayurvedic Epidemiology into Precision Population Health

Abstract

Background: Traditional medical systems like Ayurveda offer sophisticated frameworks for personalized healthcare, but integration with digital health technologies requires rigorous validation, standardized protocols, and clear regulatory pathways.

Objective: To develop and validate a comprehensive digital health informatics framework that operationalizes Ayurvedic epidemiological principles through machine learning, environmental monitoring, and predictive analytics while acknowledging validation challenges and regulatory constraints.

Methods: Mixed-methods framework development combining computational modelling of constitutional phenotypes (Prakriti), digital biomarker validation against traditional assessments, environmental risk modelling, regulatory pathway analysis for Software as Medical Device (SaMD), and economic evaluation. Inter-rater reliability, measurement validity, external benchmarking, and bias mitigation were emphasized throughout.

Results: Internal cross-validation suggested moderate-to-high classification performance for constitutional phenotyping, with positive correlations between digital biomarkers and traditional assessments. However, considering documented reliability limitations in Ayurvedic diagnostics and measurement challenges in heart rate variability and environmental predictions, these results are treated as preliminary and hypothesis-generating, requiring multi-center external validation and standardized reference methods.

Conclusions: Integrating Ayurveda with digital health is feasible and potentially impactful but must proceed with rigorous validation, transparent reporting, and stakeholder-governed ethics. This framework provides specific validation protocols, regulatory guidance, and economic evaluation standards aligned with contemporary evidence-based medicine.